Edited for clarity (#23034)
Minor changes to wording to stay consistent with professional standards.
This commit is contained in:
DataCowboy
committed by
Christopher McCormack
Christopher McCormack
parent
fe204ef8cb
commit
abeb1af705
@ -1,103 +1,103 @@
|
|||||||
---
|
---
|
||||||
title: Data Types in R
|
title: Data Types in R
|
||||||
---
|
---
|
||||||
## Scalars
|
## Scalars
|
||||||
Scalar refers to an atomic quantity that can hold only one value at a time. Scalars are the most basic data types. Some common types of scalars :
|
Scalar refers to an atomic quantity that can hold only one value at a time. Scalars are the most basic data types. Some common types of scalars :
|
||||||
|
|
||||||
1. Number
|
1. Number
|
||||||
```r
|
```r
|
||||||
> x <- 5
|
> x <- 5
|
||||||
> y <- 5.5
|
> y <- 5.5
|
||||||
> class(x)
|
> class(x)
|
||||||
[1] "numeric"
|
[1] "numeric"
|
||||||
> class(y)
|
> class(y)
|
||||||
[1] "numeric"
|
[1] "numeric"
|
||||||
> class(x+y)
|
> class(x+y)
|
||||||
[1] "numeric"
|
[1] "numeric"
|
||||||
```
|
```
|
||||||
|
|
||||||
2. Logical value
|
2. Logical value
|
||||||
```r
|
```r
|
||||||
> m <- x > y # Used to check, Is x larger than y?
|
> m <- x > y # Used to check, Is x larger than y?
|
||||||
> n <- x < y # Used to check, Is x smaller than y?
|
> n <- x < y # Used to check, Is x smaller than y?
|
||||||
> m
|
> m
|
||||||
[1] FALSE
|
[1] FALSE
|
||||||
> n
|
> n
|
||||||
[1] TRUE
|
[1] TRUE
|
||||||
> class(m)
|
> class(m)
|
||||||
[1] "logical"
|
[1] "logical"
|
||||||
> class(NA) # NA is another logical value: 'Not Available'/Missing Values
|
> class(NA) # NA is another logical value: 'Not Available'/Missing Values
|
||||||
[1] "logical"
|
[1] "logical"
|
||||||
```
|
```
|
||||||
|
|
||||||
3. Character(string)
|
3. Character(string)
|
||||||
```r
|
```r
|
||||||
> a <- "1"; b <- "2.5"
|
> a <- "1"; b <- "2.5"
|
||||||
> a;b
|
> a;b
|
||||||
[1] "1"
|
[1] "1"
|
||||||
[1] "2.5"
|
[1] "2.5"
|
||||||
> a+b
|
> a+b
|
||||||
Error in a + b : non-numeric argument to binary operator
|
Error in a + b : non-numeric argument to binary operator
|
||||||
> class(a)
|
> class(a)
|
||||||
[1] "character"
|
[1] "character"
|
||||||
> class(as.numeric(a))
|
> class(as.numeric(a))
|
||||||
[1] "numeric"
|
[1] "numeric"
|
||||||
> class(as.character(x))
|
> class(as.character(x))
|
||||||
[1] "character"
|
[1] "character"
|
||||||
```
|
```
|
||||||
|
|
||||||
## Vector
|
## Vector
|
||||||
It is a sequence of data elements of the same basic type. For example:
|
Vectors are sequences of data elements of the same basic type. For example:
|
||||||
|
|
||||||
> o <- c(1,2,5.3,6,-2,4) # Numeric vector
|
> o <- c(1,2,5.3,6,-2,4) # Numeric vector
|
||||||
> p <- c("one","two","three","four","five","six") # Character vector
|
> p <- c("one","two","three","four","five","six") # Character vector
|
||||||
> q <- c(TRUE,TRUE,FALSE,TRUE,FALSE,TRUE) # Logical vector
|
> q <- c(TRUE,TRUE,FALSE,TRUE,FALSE,TRUE) # Logical vector
|
||||||
> o;p;q
|
> o;p;q
|
||||||
[1] 1.0 2.0 5.3 6.0 -2.0 4.0
|
[1] 1.0 2.0 5.3 6.0 -2.0 4.0
|
||||||
[1] "one" "two" "three" "four" "five" "six"
|
[1] "one" "two" "three" "four" "five" "six"
|
||||||
[1] TRUE TRUE FALSE TRUE FALSE
|
[1] TRUE TRUE FALSE TRUE FALSE
|
||||||
|
|
||||||
|
|
||||||
## Matrix
|
## Matrix
|
||||||
It is a two-dimensional rectangular data set. The components in a matrix also must be of the same basic type like vector. For example:
|
A matrix is a two-dimensional rectangular data set. The components in a matrix must be of the same basic type. For example:
|
||||||
|
|
||||||
> m = matrix( c('a','a','b','c','b','a'), nrow = 2, ncol = 3, byrow = TRUE)
|
> m = matrix( c('a','a','b','c','b','a'), nrow = 2, ncol = 3, byrow = TRUE)
|
||||||
> m
|
> m
|
||||||
>[,1] [,2] [,3]
|
>[,1] [,2] [,3]
|
||||||
[1,] "a" "a" "b"
|
[1,] "a" "a" "b"
|
||||||
[2,] "c" "b" "a"
|
[2,] "c" "b" "a"
|
||||||
|
|
||||||
|
|
||||||
## Data Frame
|
## Data Frame
|
||||||
It is more general than a matrix, in that different columns can have different basic data types. For example:
|
A data frame is more general than a matrix, in that different columns can have different basic data types. For example:
|
||||||
|
|
||||||
> d <- c(1,2,3,4)
|
> d <- c(1,2,3,4)
|
||||||
> e <- c("red", "white", "red", NA)
|
> e <- c("red", "white", "red", NA)
|
||||||
> f <- c(TRUE,TRUE,TRUE,FALSE)
|
> f <- c(TRUE,TRUE,TRUE,FALSE)
|
||||||
> mydata <- data.frame(d,e,f)
|
> mydata <- data.frame(d,e,f)
|
||||||
> names(mydata) <- c("ID","Color","Passed")
|
> names(mydata) <- c("ID","Color","Passed")
|
||||||
> mydata
|
> mydata
|
||||||
|
|
||||||
ID Color Passed
|
ID Color Passed
|
||||||
1 1 red TRUE
|
1 1 red TRUE
|
||||||
2 2 white TRUE
|
2 2 white TRUE
|
||||||
3 3 red TRUE
|
3 3 red TRUE
|
||||||
4 4 <NA> FALSE
|
4 4 <NA> FALSE
|
||||||
|
|
||||||
|
|
||||||
## Lists
|
## Lists
|
||||||
It is an R-object which can contain many different types of elements inside it like vectors, functions and even another list inside it. For example:
|
Lists are R-objects which can contain many different types of elements inside them like vectors, functions and even another list. For example:
|
||||||
|
|
||||||
> list1 <- list(c(2,5,3),21.3,sin)
|
> list1 <- list(c(2,5,3),21.3,sin)
|
||||||
> list1
|
> list1
|
||||||
[[1]]
|
[[1]]
|
||||||
[1] 2 5 3
|
[1] 2 5 3
|
||||||
[[2]]
|
[[2]]
|
||||||
[1] 21.3
|
[1] 21.3
|
||||||
[[3]]
|
[[3]]
|
||||||
function (x) .Primitive("sin")
|
function (x) .Primitive("sin")
|
||||||
|
|
||||||
|
|
||||||
## Reference:
|
## Reference:
|
||||||
* [Official Docs](https://cran.r-project.org/manuals.html)
|
* [Official Docs](https://cran.r-project.org/manuals.html)
|
||||||
* [Data Types in R by r-bloggers](https://www.r-bloggers.com/classes-and-objects-in-r/)
|
* [Data Types in R by r-bloggers](https://www.r-bloggers.com/classes-and-objects-in-r/)
|
||||||
|
|||||||
Reference in New Issue
Block a user